Machine Learning Researcher (PhD) - Systematic Commodities Hedge Fund
Machine Learning Researcher (PhD) – Systematic Commodities Hedge Fund
Moreton Capital Partners is seeking a Machine Learning Researcher to help design and improve the predictive models that power our systematic commodities trading strategies.
We trade global commodity futures using machine learning, alternative data, and institutional-grade portfolio construction. Our edge comes from research depth, disciplined experimentation, and robust production systems.
This role is for candidates completing or having recently completed a PhD with a strong machine learning, statistics, or applied mathematics focus who want to apply advanced research in a real capital environment.
You will work directly with the CIO and quant research team to turn cutting-edge ML ideas into live trading signals.
This is not a purely academic role.
Your research will ship to production and directly impact portfolio returns.
What you will work on
- Designing predictive models for cross-sectional and time-series commodity returns
- Developing new features from price, positioning, options, macro, and alternative datasets
- Improving signal robustness and reducing overfitting through rigorous validation
- Combining and blending multiple models into portfolio-level forecasts
- Regime detection, meta-models, and adaptive allocation frameworks
- Model diagnostics, explainability, and stability analysis
- Translating research ideas into production-ready implementations
- Collaborating with engineers to deploy models into live trading systems
Key Responsibilities
- Formulate research hypotheses and test them using clean, time-aware ML pipelines
- Build and evaluate models (tree-based, linear, ensemble, deep learning, etc.)
- Run walk-forward and out-of-sample experiments with realistic costs
- Analyze information coefficients, turnover, drawdowns, and risk-adjusted returns
- Design feature engineering frameworks and reusable research tooling
- Document findings clearly and communicate results to portfolio managers
- Contribute to improving research standards, reproducibility, and processes
Requirements
- PhD (completed or near completion) in Machine Learning, Statistics, Applied Mathematics, Computer Science, Physics, Engineering, or related quantitative field
- Strong Python skills and experience with scientific computing stacks
- Deep understanding of statistical learning and model validation
- Experience working with large datasets and experimental pipelines
- Ability to move from theory to practical implementation
- Intellectual curiosity and strong problem-solving mindset
- Comfortable working in a fast-paced, high-ownership environment
Bonus Points For
- Experience with financial markets or systematic trading
- Familiarity with time-series modelling or forecasting
- Experience with LightGBM/XGBoost, deep learning, or ensemble methods
- Exposure to portfolio construction or risk modelling
- Experience with cloud or distributed compute environments
- Published research or strong applied projects
Why this role is unique
- Direct impact: your research drives live trading capital
- Research freedom: explore ideas with fast feedback loops
- Real-world data: large, messy, multi-source datasets
- Small team: high ownership and rapid iteration
- Strong learning curve across ML, markets, and portfolio construction
- Clear path into Senior Researcher or Portfolio Manager responsibilities
Benefits
- Market leading benefits
- High responsibility from day one
- Performance bonus tied to firm growth and personal performance (up to 3x salary)